Exploration of the trait analysis for trait synchrony.

Parameters: environmental correction is FALSE.

Hypotheses

Check all hypotheses between traits and environmental drivers, and among traits

Big PCA

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Arthropods (above, herbivores)
SLA ++ D
  • disturbance favours fast-growing species
NA
  • more nutrients allow for ‘cheap’ leaves
NA 0.35 0.2 - 0.5 0.0000671 0.48 0.12 0.01 0.35 0.2 - 0.5
Seed_mass D
  • disturbance selects for smaller seeds (= colonisation)
Diaz et al. 2016 NA NA -0.29 -0.45 - -0.14 0.0008729 -0.37 -0.35 0.04 -0.29 -0.45 - -0.14
LDMC D Tougher = slow NA NA NA -0.36 -0.51 - -0.21 0.0000000 -0.37 -0.52 0.01 -0.36 -0.51 - -0.21
LeafN ++ D NA NA
  • more nutrients allow for ‘cheap’ leaves
Diaz et al. 2016 0.48 0.34 - 0.62 0.0000000 0.47 0.56 0.03 0.48 0.34 - 0.62
Arthropods (above, omnicarnivores)
LeafP ++ D NA NA
  • more nutrients allow for ‘cheap’ leaves
NA 0.53 0.39 - 0.66 0.0000000 0.41 0.53 0.14 0.53 0.39 - 0.66
Root_tissue_density D NA NA
  • conservative and/or ‘collaboration’ => if few nutrients, need for collaboration
Bergmann et al. 2020 -0.27 -0.42 - -0.11 0.0024022 -0.38 -0.12 0.02 -0.27 -0.42 - -0.11
Arthropods (below, herbivores)
Aoc_BodySize D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 NA NA 0.08 -0.08 - 0.25 0.4014453 Inconclusive 0.11 -0.27 0.08 0.08 -0.08 - 0.25 Inconclusive
Aoc_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 NA NA 0.32 0.17 - 0.48 0.0003525 0.67 0.34 -0.27 0.32 0.17 - 0.48
Ah_BodySize +/- D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 NA NA -0.26 -0.42 - -0.1 0.0030785 B -0.26 -0.28 -0.03 -0.26 -0.42 - -0.1 B
Arthropods (below, predators)
Ah_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 NA NA 0.28 0.13 - 0.44 0.0012847 0.32 0.45 -0.04 0.28 0.13 - 0.44
Ah_Generalism ++ NA NA NA NA NA 0.39 0.24 - 0.54 0.0000000 0.14 0.41 0.21 0.39 0.24 - 0.54
Bats
Ah_Generations ++ I NA NA NA NA 0.54 0.4 - 0.68 0.0000000 0.81 0.47 -0.12 0.54 0.4 - 0.68
Aoc_b_BodySize D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 NA NA -0.01 -0.17 - 0.15 0.9357700 Inconclusive 0.13 0.02 -0.08 -0.01 -0.17 - 0.15 Inconclusive
Aoc_b_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 NA NA 0.11 -0.05 - 0.27 0.2588871 Inconclusive -0.04 -0.05 0.18 0.11 -0.05 - 0.27 Inconclusive
Birds (insectivorous)
Ah_b_BodySize D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 NA NA -0.28 -0.46 - -0.1 0.0062122 -0.43 -0.44 0.22 -0.25 -0.41 - -0.09
Ah_b_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 NA NA 0.16 -0.02 - 0.35 0.1305387 Inconclusive 0.04 0.14 0.12 0.15 -0.01 - 0.32 Inconclusive
Ah_b_Generalism ++ NA NA NA NA NA -0.09 -0.28 - 0.1 0.4416857 Inconclusive 0.13 0.03 -0.28 -0.09 -0.28 - 0.1 Inconclusive
Bi_Size ++ NA more = fast NA NA NA 0.31 0.16 - 0.47 0.0005640 0.30 0.17 0.10 0.31 0.16 - 0.47
Bi_Incub ++ NA more = disturbance NA NA NA 0.18 0.02 - 0.34 0.0518988 0.32 0.07 -0.03 0.18 0.02 - 0.34
Butterflies
Bi_TOffsprings I Small = fast NA NA NA -0.15 -0.31 - 0.01 0.1079747 Inconclusive -0.38 -0.06 0.10 -0.15 -0.31 - 0.01 Inconclusive
Bi_GenLength NA NA NA NA NA 0.27 0.11 - 0.42 0.0029932 0.32 0.17 0.03 0.27 0.11 - 0.42
Bi_AgeMax I Small = fast NA NA NA 0.30 0.15 - 0.46 0.0007231 0.23 0.20 0.12 0.30 0.15 - 0.46
but_flight ++ NA Early emergence favorable bc disturbance starts early Börschig et al. 2013 NA NA 0.25 0.09 - 0.41 0.0047448 0.77 0.36 -0.37 0.25 0.09 - 0.41
but_GenYear ++ NA High reproduction can compensate mortality due to disturbance Börschig et al. 2013 NA NA 0.23 0.07 - 0.39 0.0104904 0.11 0.07 0.15 0.23 0.07 - 0.39
Collembola
but_hibernation ++ NA Later hibernation stage means butterflies ready before disturbance NA NA NA 0.24 0.08 - 0.4 0.0062122 0.73 0.30 -0.33 0.25 0.09 - 0.41
but_Size ++ NA Larger wings = more dispersal Börschig et al. 2013 NA NA -0.12 -0.28 - 0.05 0.2292461 Inconclusive 0.06 0.07 -0.21 -0.13 -0.29 - 0.03 Inconclusive
but_Generalism ++ NA NA NA High nutrients = low plant diversity, hence more generalists NA 0.28 0.12 - 0.44 0.0018800 0.12 0.03 0.22 0.28 0.12 - 0.44
col_Sex NA NA NA NA NA 0.01 -0.16 - 0.18 0.9411600 Inconclusive 0.03 0.08 -0.04 0.01 -0.16 - 0.17 Inconclusive
Microbes
col_Gen_per_Year ++ NA Furca = escape disturbance NA NA NA -0.09 -0.26 - 0.08 0.3999297 Inconclusive 0.26 -0.01 -0.30 -0.09 -0.26 - 0.08 Inconclusive
col_Depth ++ NA NA NA NA NA -0.01 -0.18 - 0.16 0.9285170 Inconclusive -0.07 -0.09 0.06 -0.01 -0.18 - 0.16 Inconclusive
col_Size NA a/ more resources or b/ selected NA NA NA 0.04 -0.13 - 0.21 0.7161345 Inconclusive -0.23 -0.09 0.24 0.04 -0.13 - 0.21 Inconclusive
mites_DaysAdult NA longer lifespan = slow NA NA NA -0.05 -0.22 - 0.12 0.6927679 Inconclusive 0.08 -0.14 -0.06 -0.05 -0.22 - 0.12 Inconclusive
mites_Mass NA Mass = size = slow NA NA NA -0.16 -0.32 - 0.01 0.1046641 Inconclusive 0.19 -0.12 -0.30 -0.16 -0.32 - 0.01 Inconclusive
Mites
mites_Sex I Habitat openness = sexual repro, more LUI = more growth NA NA NA 0.01 -0.16 - 0.18 0.9351016 Inconclusive 0.28 0.01 -0.21 0.01 -0.16 - 0.18 Inconclusive
mites_Hab_spec ++ NA more disturbance = more in the soil NA NA NA -0.03 -0.2 - 0.14 0.8138542 Inconclusive -0.05 -0.10 0.02 -0.03 -0.2 - 0.14 Inconclusive
mites_Feed_spec ++ NA NA NA NA NA 0.12 -0.04 - 0.29 0.2139969 Inconclusive 0.23 0.07 -0.06 0.12 -0.04 - 0.29 Inconclusive
P_patho ++ NA More bacteria = more bacterivores NA NA NA 0.46 0.32 - 0.61 0.0000000 0.45 0.40 0.08 0.46 0.32 - 0.61
Pb_Size NA a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size NA NA NA -0.27 -0.43 - -0.11 0.0022118 -0.24 -0.09 -0.14 -0.27 -0.43 - -0.11
Plants (AG)
Ps_Size NA a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size NA NA NA -0.31 -0.46 - -0.15 0.0005875 -0.06 -0.15 -0.28 -0.31 -0.46 - -0.15
mic_FB D and I Fungi slower than bacteria, linked to slow plant traits NA Fungi dominate in low-nutrient soils; associated with slow plants de Vries et al. 2006, de Vries et al. 2012, Boeddinghaus et al. 2019 -0.24 -0.4 - -0.08 0.0062863 -0.47 -0.43 0.20 -0.24 -0.4 - -0.08
mic_Fpathotroph ++ NA Fast= more parasites check https://www.nature.com/articles/s41467-021-23605-y Fast plant invest less in defenses, so more pathogens NA 0.31 0.15 - 0.46 0.0005640 0.21 0.51 0.05 0.31 0.15 - 0.46
mic_O.C.ratio NA NA NA
  • more oligo (Acido) than copio (Actibo, alphapto…) in slow
Check Neff 2015, Ramirez 2010, Fierer 2012 -0.04 -0.2 - 0.12 0.6947517 Inconclusive 0.07 -0.12 -0.03 -0.04 -0.2 - 0.12 Inconclusive
mic_Bvolume +/- D a/ small cells more efficient for diffusive uptake (-) OR b/ for a given substrate demand, large radius compensates low substrate concentrations Westoby et al. 2021 NA NA -0.31 -0.46 - -0.15 0.0005875 B -0.09 -0.10 -0.25 -0.31 -0.46 - -0.15 B
Plants (BG)
mic_Bgenome_size D NA NA
  • Larger genomes should be more successful in resource-poor environments v. S strategies have smaller genomes (Westoby et al.)
Leff et al. 2015; Konstantinidis (2004) -0.04 -0.21 - 0.12 0.6947517 Inconclusive -0.27 -0.48 0.33 -0.04 -0.21 - 0.12 Inconclusive
Protists
bat_mass NA Large more sensitive to disturbance BUT large less sensitive to urbanisation?? Farneda et al. 2015, Moir et al. 2021 , Jung et al 2018 NA NA -0.17 -0.33 - -0.01 0.0624926 0.03 0.14 -0.29 -0.17 -0.33 - -0.01
Protists bacterivores
bat_lifespan NA NA NA NA NA NA 0.06 -0.1 - 0.23 0.5392508 Inconclusive -0.18 -0.30 0.30 0.06 -0.1 - 0.23 Inconclusive
Protists predators
bat_offspring ++ NA opposite to size NA NA NA -0.16 -0.32 - 0.01 0.0983643 Inconclusive 0.15 0.27 -0.39 -0.16 -0.32 - 0.01 Inconclusive

Identification of strategy axes for each group

Plants, above- and below-ground

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Plants (AG)
SLA ++ D
  • disturbance favours fast-growing species
NA
  • more nutrients allow for ‘cheap’ leaves
NA 0.35 0.2 - 0.5 0.0000671 0.48 0.12 0.01 0.35 0.2 - 0.5
Seed_mass D
  • disturbance selects for smaller seeds (= colonisation)
Diaz et al. 2016 NA NA -0.29 -0.45 - -0.14 0.0008729 -0.37 -0.35 0.04 -0.29 -0.45 - -0.14
LDMC D Tougher = slow NA NA NA -0.36 -0.51 - -0.21 0.0000000 -0.37 -0.52 0.01 -0.36 -0.51 - -0.21
LeafN ++ D NA NA
  • more nutrients allow for ‘cheap’ leaves
Diaz et al. 2016 0.48 0.34 - 0.62 0.0000000 0.47 0.56 0.03 0.48 0.34 - 0.62
LeafP ++ D NA NA
  • more nutrients allow for ‘cheap’ leaves
NA 0.53 0.39 - 0.66 0.0000000 0.41 0.53 0.14 0.53 0.39 - 0.66
Plants (BG)
Root_tissue_density D NA NA
  • conservative and/or ‘collaboration’ => if few nutrients, need for collaboration
Bergmann et al. 2020 -0.27 -0.42 - -0.11 0.0024022 -0.38 -0.12 0.02 -0.27 -0.42 - -0.11
## [1] "Plants, All"
## Registered S3 methods overwritten by 'car':
##   method                          from
##   influence.merMod                lme4
##   cooks.distance.influence.merMod lme4
##   dfbeta.influence.merMod         lme4
##   dfbetas.influence.merMod        lme4

Bacteria & fungi

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Microbes
mic_FB D and I Fungi slower than bacteria, linked to slow plant traits NA Fungi dominate in low-nutrient soils; associated with slow plants de Vries et al. 2006, de Vries et al. 2012, Boeddinghaus et al. 2019 -0.24 -0.4 - -0.08 0.0062863 -0.47 -0.43 0.20 -0.24 -0.4 - -0.08
mic_Fpathotroph ++ NA Fast= more parasites check https://www.nature.com/articles/s41467-021-23605-y Fast plant invest less in defenses, so more pathogens NA 0.31 0.15 - 0.46 0.0005640 0.21 0.51 0.05 0.31 0.15 - 0.46
mic_O.C.ratio NA NA NA
  • more oligo (Acido) than copio (Actibo, alphapto…) in slow
Check Neff 2015, Ramirez 2010, Fierer 2012 -0.04 -0.2 - 0.12 0.6947517 Inconclusive 0.07 -0.12 -0.03 -0.04 -0.2 - 0.12 Inconclusive
mic_Bvolume +/- D a/ small cells more efficient for diffusive uptake (-) OR b/ for a given substrate demand, large radius compensates low substrate concentrations Westoby et al. 2021 NA NA -0.31 -0.46 - -0.15 0.0005875 B -0.09 -0.10 -0.25 -0.31 -0.46 - -0.15 B
mic_Bgenome_size D NA NA
  • Larger genomes should be more successful in resource-poor environments v. S strategies have smaller genomes (Westoby et al.)
Leff et al. 2015; Konstantinidis (2004) -0.04 -0.21 - 0.12 0.6947517 Inconclusive -0.27 -0.48 0.33 -0.04 -0.21 - 0.12 Inconclusive

Arthropods, above-ground

Herbivores

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Arthropods (above, herbivores)
Aoc_BodySize D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 NA NA 0.08 -0.08 - 0.25 0.4014453 Inconclusive 0.11 -0.27 0.08 0.08 -0.08 - 0.25 Inconclusive
Aoc_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 NA NA 0.32 0.17 - 0.48 0.0003525 0.67 0.34 -0.27 0.32 0.17 - 0.48
Ah_BodySize +/- D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 NA NA -0.26 -0.42 - -0.1 0.0030785 B -0.26 -0.28 -0.03 -0.26 -0.42 - -0.1 B
Ah_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 NA NA 0.28 0.13 - 0.44 0.0012847 0.32 0.45 -0.04 0.28 0.13 - 0.44
Arthropods (above, omnicarnivores)
Ah_Generalism ++ NA NA NA NA NA 0.39 0.24 - 0.54 0.0000000 0.14 0.41 0.21 0.39 0.24 - 0.54
Ah_Generations ++ I NA NA NA NA 0.54 0.4 - 0.68 0.0000000 0.81 0.47 -0.12 0.54 0.4 - 0.68
Arthropods (below, herbivores)
Aoc_b_BodySize D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 NA NA -0.01 -0.17 - 0.15 0.9357700 Inconclusive 0.13 0.02 -0.08 -0.01 -0.17 - 0.15 Inconclusive
Aoc_b_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 NA NA 0.11 -0.05 - 0.27 0.2588871 Inconclusive -0.04 -0.05 0.18 0.11 -0.05 - 0.27 Inconclusive
Ah_b_BodySize D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 NA NA -0.28 -0.46 - -0.1 0.0062122 -0.43 -0.44 0.22 -0.25 -0.41 - -0.09
Arthropods (below, predators)
Ah_b_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 NA NA 0.16 -0.02 - 0.35 0.1305387 Inconclusive 0.04 0.14 0.12 0.15 -0.01 - 0.32 Inconclusive
Ah_b_Generalism ++ NA NA NA NA NA -0.09 -0.28 - 0.1 0.4416857 Inconclusive 0.13 0.03 -0.28 -0.09 -0.28 - 0.1 Inconclusive
Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Butterflies
but_flight ++ NA Early emergence favorable bc disturbance starts early Börschig et al. 2013 NA NA 0.25 0.09 - 0.41 0.0047448 0.77 0.36 -0.37 0.25 0.09 - 0.41
but_GenYear ++ NA High reproduction can compensate mortality due to disturbance Börschig et al. 2013 NA NA 0.23 0.07 - 0.39 0.0104904 0.11 0.07 0.15 0.23 0.07 - 0.39
but_hibernation ++ NA Later hibernation stage means butterflies ready before disturbance NA NA NA 0.24 0.08 - 0.4 0.0062122 0.73 0.30 -0.33 0.25 0.09 - 0.41
but_Size ++ NA Larger wings = more dispersal Börschig et al. 2013 NA NA -0.12 -0.28 - 0.05 0.2292461 Inconclusive 0.06 0.07 -0.21 -0.13 -0.29 - 0.03 Inconclusive
but_Generalism ++ NA NA NA High nutrients = low plant diversity, hence more generalists NA 0.28 0.12 - 0.44 0.0018800 0.12 0.03 0.22 0.28 0.12 - 0.44

Predators

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Arthropods (above, omnicarnivores)
Aoc_BodySize D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 NA NA 0.08 -0.08 - 0.25 0.4014453 Inconclusive 0.11 -0.27 0.08 0.08 -0.08 - 0.25 Inconclusive
Aoc_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 NA NA 0.32 0.17 - 0.48 0.0003525 0.67 0.34 -0.27 0.32 0.17 - 0.48
Arthropods (below, predators)
Aoc_b_BodySize D Smaller species evade disturbance more easily Birkhofer et al. 2015, 2017, see also Blake 1994, Neff et al. 2019, Simons et al. 2016 NA NA -0.01 -0.17 - 0.15 0.9357700 Inconclusive 0.13 0.02 -0.08 -0.01 -0.17 - 0.15 Inconclusive
Aoc_b_Dispersal ++ D Disturbance selects for good dispersers to recolonize the plot after disturbance, Hanson et al. 2016 Birkhofer et al. 2013, 2015, 2017 , Simons et al. 2016 NA NA 0.11 -0.05 - 0.27 0.2588871 Inconclusive -0.04 -0.05 0.18 0.11 -0.05 - 0.27 Inconclusive

Protists

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Protists
P_patho ++ NA More bacteria = more bacterivores NA NA NA 0.46 0.32 - 0.61 0.0000000 0.45 0.40 0.08 0.46 0.32 - 0.61
Protists bacterivores
Pb_Size NA a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size NA NA NA -0.27 -0.43 - -0.11 0.0022118 -0.24 -0.09 -0.14 -0.27 -0.43 - -0.11
Protists predators
Ps_Size NA a/ More nutrients = more resources to grow. b/ Disturbance selects smaller size NA NA NA -0.31 -0.46 - -0.15 0.0005875 -0.06 -0.15 -0.28 -0.31 -0.46 - -0.15

Birds

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Birds (insectivorous)
Bi_Size ++ NA more = fast NA NA NA 0.31 0.16 - 0.47 0.0005640 0.30 0.17 0.10 0.31 0.16 - 0.47
Bi_Incub ++ NA more = disturbance NA NA NA 0.18 0.02 - 0.34 0.0518988 0.32 0.07 -0.03 0.18 0.02 - 0.34
Bi_TOffsprings I Small = fast NA NA NA -0.15 -0.31 - 0.01 0.1079747 Inconclusive -0.38 -0.06 0.10 -0.15 -0.31 - 0.01 Inconclusive
Bi_GenLength NA NA NA NA NA 0.27 0.11 - 0.42 0.0029932 0.32 0.17 0.03 0.27 0.11 - 0.42
Bi_AgeMax I Small = fast NA NA NA 0.30 0.15 - 0.46 0.0007231 0.23 0.20 0.12 0.30 0.15 - 0.46

Mites & collembola

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Mites
mites_DaysAdult NA longer lifespan = slow NA NA NA -0.05 -0.22 - 0.12 0.6927679 Inconclusive 0.08 -0.14 -0.06 -0.05 -0.22 - 0.12 Inconclusive
mites_Mass NA Mass = size = slow NA NA NA -0.16 -0.32 - 0.01 0.1046641 Inconclusive 0.19 -0.12 -0.30 -0.16 -0.32 - 0.01 Inconclusive
mites_Sex I Habitat openness = sexual repro, more LUI = more growth NA NA NA 0.01 -0.16 - 0.18 0.9351016 Inconclusive 0.28 0.01 -0.21 0.01 -0.16 - 0.18 Inconclusive
mites_Hab_spec ++ NA more disturbance = more in the soil NA NA NA -0.03 -0.2 - 0.14 0.8138542 Inconclusive -0.05 -0.10 0.02 -0.03 -0.2 - 0.14 Inconclusive
mites_Feed_spec ++ NA NA NA NA NA 0.12 -0.04 - 0.29 0.2139969 Inconclusive 0.23 0.07 -0.06 0.12 -0.04 - 0.29 Inconclusive
## NULL

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Collembola
col_Sex NA NA NA NA NA 0.01 -0.16 - 0.18 0.9411600 Inconclusive 0.03 0.08 -0.04 0.01 -0.16 - 0.17 Inconclusive
col_Gen_per_Year ++ NA Furca = escape disturbance NA NA NA -0.09 -0.26 - 0.08 0.3999297 Inconclusive 0.26 -0.01 -0.30 -0.09 -0.26 - 0.08 Inconclusive
col_Depth ++ NA NA NA NA NA -0.01 -0.18 - 0.16 0.9285170 Inconclusive -0.07 -0.09 0.06 -0.01 -0.18 - 0.16 Inconclusive
col_Size NA a/ more resources or b/ selected NA NA NA 0.04 -0.13 - 0.21 0.7161345 Inconclusive -0.23 -0.09 0.24 0.04 -0.13 - 0.21 Inconclusive
## NULL

Bats

Trait_short Expected_direction Direct/indirect Disturbance ref_dist Resources ref_res Est CI Padj Fit_exp Mowing Grazing Fertil Est_nocorr CI_nocorr Fit_exp_nocorr
Bats
bat_mass NA Large more sensitive to disturbance BUT large less sensitive to urbanisation?? Farneda et al. 2015, Moir et al. 2021 , Jung et al 2018 NA NA -0.17 -0.33 - -0.01 0.0624926 0.03 0.14 -0.29 -0.17 -0.33 - -0.01
bat_lifespan NA NA NA NA NA NA 0.06 -0.1 - 0.23 0.5392508 Inconclusive -0.18 -0.30 0.30 0.06 -0.1 - 0.23 Inconclusive
bat_offspring ++ NA opposite to size NA NA NA -0.16 -0.32 - 0.01 0.0983643 Inconclusive 0.15 0.27 -0.39 -0.16 -0.32 - 0.01 Inconclusive
## NULL

Try to do a SEM

## 
##  Pearson's product-moment correlation
## 
## data:  PCA_pca$ind$coord[, 1] and env_data_lui[Plot %in% dd$Plot, ]$LUI
## t = 6.5034, df = 148, p-value = 1.135e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3366118 0.5873277
## sample estimates:
##      cor 
## 0.471441

Run above-ground model, simple

## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
##            cfi          rmsea rmsea.ci.upper            bic 
##          0.979          0.181          0.333       2400.988
## quartz_off_screen 
##                 2

Run below-ground model, simple

## lavaan 0.6-9 ended normally after 19 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        40
##                                                       
##                                                   Used       Total
##   Number of observations                           120         150
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                12.932
##   Degrees of freedom                                 4
##   P-value (Chi-square)                           0.012
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   plant ~                                                                     
##     LUI                     0.568    0.077    7.368    0.000    0.568    0.558
##   Protists_patho ~                                                            
##     LUI                     0.291    0.092    3.152    0.002    0.291    0.285
##     plant                   0.364    0.091    4.008    0.000    0.364    0.362
##   Microbes ~                                                                  
##     LUI                    -0.066    0.087   -0.757    0.449   -0.066   -0.069
##     plant                   0.571    0.086    6.650    0.000    0.571    0.603
##   Protists_bact ~                                                             
##     LUI                     0.270    0.103    2.620    0.009    0.270    0.286
##     plant                   0.049    0.119    0.410    0.682    0.049    0.053
##     Microbes               -0.158    0.103   -1.533    0.125   -0.158   -0.162
##     Protists_patho          0.001    0.097    0.011    0.992    0.001    0.001
##   Protists_sec ~                                                              
##     LUI                     0.197    0.103    1.922    0.055    0.197    0.210
##     Protists_bact          -0.056    0.088   -0.639    0.523   -0.056   -0.057
##     plant                  -0.012    0.115   -0.101    0.920   -0.012   -0.013
##     Microbes                0.167    0.101    1.652    0.098    0.167    0.172
##     Protists_patho          0.119    0.094    1.260    0.208    0.119    0.130
##   Mites ~                                                                     
##     LUI                     0.037    0.109    0.341    0.733    0.037    0.039
##     plant                   0.122    0.121    1.004    0.315    0.122    0.129
##     Microbes                0.190    0.107    1.771    0.077    0.190    0.191
##     Protists_sec           -0.031    0.096   -0.325    0.745   -0.031   -0.030
##     Protists_bact           0.124    0.093    1.327    0.184    0.124    0.121
##     Protists_patho         -0.234    0.100   -2.341    0.019   -0.234   -0.249
##   Coll ~                                                                      
##     LUI                     0.043    0.115    0.378    0.705    0.043    0.044
##     Microbes               -0.201    0.113   -1.784    0.074   -0.201   -0.197
##     plant                   0.143    0.127    1.121    0.262    0.143    0.148
##     Protists_sec           -0.064    0.101   -0.634    0.526   -0.064   -0.061
##     Protists_bact          -0.105    0.098   -1.078    0.281   -0.105   -0.101
##     Protists_patho         -0.117    0.105   -1.116    0.264   -0.117   -0.122
##   Arth_omnicarni_below ~                                                      
##     LUI                     0.094    0.102    0.922    0.356    0.094    0.097
##     Mites                   0.353    0.086    4.096    0.000    0.353    0.349
##     Coll                   -0.230    0.082   -2.801    0.005   -0.230   -0.234
##     Protists_sec           -0.022    0.091   -0.240    0.810   -0.022   -0.021
##     plant                  -0.112    0.103   -1.089    0.276   -0.112   -0.118
##     Protists_patho          0.048    0.099    0.489    0.625    0.048    0.051
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .plant             0.761    0.098    7.746    0.000    0.761    0.689
##    .Protists_patho    0.753    0.097    7.746    0.000    0.753    0.673
##    .Microbes          0.672    0.087    7.746    0.000    0.672    0.678
##    .Protists_bact     0.858    0.111    7.746    0.000    0.858    0.906
##    .Protists_sec      0.804    0.104    7.746    0.000    0.804    0.862
##    .Mites             0.889    0.115    7.746    0.000    0.889    0.905
##    .Coll              0.982    0.127    7.746    0.000    0.982    0.951
##    .Arth_mncrn_blw    0.822    0.106    7.746    0.000    0.822    0.819
## lavaan 0.6-9 ended normally after 19 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        36
##                                                       
##                                                   Used       Total
##   Number of observations                           120         150
##                                                                   
## Model Test User Model:
##                                                       
##   Test statistic                                19.371
##   Degrees of freedom                                 8
##   P-value (Chi-square)                           0.013
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Regressions:
##                          Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   plant ~                                                                     
##     LUI                     0.568    0.077    7.368    0.000    0.568    0.558
##   Protists_patho ~                                                            
##     LUI                     0.289    0.092    3.128    0.002    0.289    0.282
##     plant                   0.368    0.091    4.058    0.000    0.368    0.366
##   Microbes ~                                                                  
##     LUI                    -0.066    0.087   -0.757    0.449   -0.066   -0.069
##     plant                   0.571    0.086    6.650    0.000    0.571    0.603
##   Protists_bact ~                                                             
##     LUI                     0.270    0.099    2.729    0.006    0.270    0.287
##     plant                   0.049    0.113    0.431    0.666    0.049    0.053
##     Microbes               -0.158    0.103   -1.530    0.126   -0.158   -0.161
##   Protists_sec ~                                                              
##     LUI                     0.234    0.099    2.356    0.018    0.234    0.249
##     plant                   0.011    0.111    0.098    0.922    0.011    0.012
##     Microbes                0.203    0.101    2.002    0.045    0.203    0.208
##     Protists_bact          -0.056    0.089   -0.634    0.526   -0.056   -0.056
##   Mites ~                                                                     
##     LUI                    -0.030    0.108   -0.277    0.782   -0.030   -0.032
##     plant                   0.078    0.118    0.659    0.510    0.078    0.083
##     Microbes                0.123    0.110    1.117    0.264    0.123    0.124
##     Protists_sec           -0.055    0.097   -0.562    0.574   -0.055   -0.054
##     Protists_bact           0.122    0.095    1.284    0.199    0.122    0.121
##   Coll ~                                                                      
##     LUI                     0.010    0.112    0.087    0.931    0.010    0.010
##     plant                   0.121    0.122    0.989    0.323    0.121    0.124
##     Microbes               -0.234    0.114   -2.061    0.039   -0.234   -0.229
##     Protists_bact          -0.106    0.098   -1.081    0.280   -0.106   -0.101
##     Protists_sec           -0.076    0.101   -0.752    0.452   -0.076   -0.072
##   Arth_omnicarni_below ~                                                      
##     LUI                     0.023    0.087    0.263    0.793    0.023    0.024
##     Mites                   0.332    0.084    3.926    0.000    0.332    0.324
##     Coll                   -0.222    0.081   -2.730    0.006   -0.222   -0.226
##     Protists_sec           -0.022    0.090   -0.243    0.808   -0.022   -0.021
##     Protists_bact           0.127    0.089    1.430    0.153    0.127    0.123
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .Protists_patho ~~                                                      
##    .Arth_mncrn_blw     0.039    0.072    0.539    0.590    0.039    0.049
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .plant             0.761    0.098    7.746    0.000    0.761    0.689
##    .Protists_patho    0.753    0.097    7.746    0.000    0.753    0.672
##    .Microbes          0.672    0.087    7.746    0.000    0.672    0.678
##    .Protists_bact     0.858    0.111    7.746    0.000    0.858    0.907
##    .Protists_sec      0.814    0.105    7.746    0.000    0.814    0.864
##    .Mites             0.927    0.120    7.746    0.000    0.927    0.959
##    .Coll              0.991    0.128    7.746    0.000    0.991    0.951
##    .Arth_mncrn_blw    0.817    0.105    7.746    0.000    0.817    0.809
##            cfi          rmsea rmsea.ci.upper            bic 
##          0.940          0.109          0.172       2704.408
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
## [1] "Direct"
## [1] "Indirect"
## [1] "Total"
##            cfi          rmsea rmsea.ci.upper            bic 
##          0.999          0.012          0.097       3262.850
## quartz_off_screen 
##                 2

Use the parameters defined in the simple SEM

## 
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
## 
##     discard
## The following object is masked from 'package:readr':
## 
##     col_factor
## The following object is masked from 'package:viridis':
## 
##     viridis_pal
## Scale for 'size' is already present. Adding another scale for 'size', which
## will replace the existing scale.

## Run above-ground model, full model

Run below-ground model, full lui components

Use the parameters defined in the complex SEMs

get multidiv

## `set.seed(1)` was used to initialize repeatable random subsampling.
## Please record this for your records so others can reproduce.
## Try `set.seed(1); .Random.seed` for the full vector
## ...
## 50OTUs were removed because they are no longer 
## present in any sample after random subsampling
## ...
## `set.seed(1)` was used to initialize repeatable random subsampling.
## Please record this for your records so others can reproduce.
## Try `set.seed(1); .Random.seed` for the full vector
## ...
## 11288OTUs were removed because they are no longer 
## present in any sample after random subsampling
## ...

Check effect on EF MF

## [1] 496
## [1] 496
## quartz_off_screen 
##                 2
## quartz_off_screen 
##                 2
## 
##  Pearson's product-moment correlation
## 
## data:  Dim.1_all and Dim.1_fun
## t = 5.8402, df = 148, p-value = 3.184e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2928258 0.5545752
## sample estimates:
##       cor 
## 0.4327777
## Start:  AIC=35.65
## Dim.1_fun ~ Dim.1_all
## 
##             Df Sum of Sq    RSS    AIC
## <none>                   185.24 35.654
## - Dim.1_all  1    42.691 227.93 64.762
## Start:  AIC=51.94
## Dim.1_fun ~ Dim.1_mic
## 
##             Df Sum of Sq    RSS    AIC
## <none>                   206.48 51.938
## - Dim.1_mic  1    21.448 227.93 64.762
## Start:  AIC=58.47
## Dim.1_fun ~ Dim.1_plants
## 
##                Df Sum of Sq    RSS    AIC
## <none>                      215.68 58.472
## - Dim.1_plants  1    12.255 227.93 64.762
## Start:  AIC=53.07
## Dim.1_fun ~ multidiv
## 
##            Df Sum of Sq    RSS    AIC
## <none>                  208.05 53.073
## - multidiv  1     19.88 227.93 64.762
## Start:  AIC=53.87
## Dim.1_fun ~ LUI
## 
##        Df Sum of Sq    RSS    AIC
## <none>              209.16 53.866
## - LUI   1    18.777 227.93 64.762
##                                                Model Estimate (sd)
## 1   Functions slow-fast ~ entire community slow-fast   0.29 (0.05)
## 2             Functions slow-fast ~ plants slow-fast   0.16 (0.06)
## 3 Functions slow-fast ~ bacteria and fungi slow-fast  -0.25 (0.06)
## 4                          Functions slow-fast ~ LUI    0.35 (0.1)
## 5     Functions slow-fast ~ taxonomic multidiversity   -0.37 (0.1)
##                   Pval   R2        adj.P
## 1 3.18431138695422e-08 0.18 1.592156e-07
## 2   0.0043017352183886 0.05 4.301735e-03
## 3 0.000134527981309077 0.09 3.363200e-04
## 4 0.000369319089321567 0.08 4.616489e-04
## 5 0.000243567565734863 0.08 4.059459e-04
## lavaan 0.6-9 ended normally after 16 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         5
##                                                       
##   Number of observations                           150
##                                                       
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws            1000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   Dim.1_fun ~                                         
##     LUI        (d)    0.132    0.087    1.511    0.131
##     Dim.1_all  (a)    0.254    0.059    4.306    0.000
##   Dim.1_all ~                                         
##     LUI        (b)    0.878    0.150    5.874    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .Dim.1_fun         1.221    0.144    8.455    0.000
##    .Dim.1_all         2.681    0.290    9.258    0.000
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)
##     indirect          0.223    0.066    3.403    0.001
##     total             0.355    0.081    4.404    0.000
##     diff              0.091    0.132    0.691    0.490
## quartz_off_screen 
##                 2